Speech endpoint detection method based on logarithmic energy entropy product of adaptive sub-bands in low signal-to-noise ratio environments Online publication date: Tue, 30-Apr-2024
by MingHui Zhu; Peng-Cheng Huang; JiaYong Zhang
International Journal of Biometrics (IJBM), Vol. 16, No. 3/4, 2024
Abstract: In this paper, a detection method based on logarithmic energy entropy product of adaptive sub-bands is designed. After the speech signal is divided into frames and FFT, the probability of the existence of the speech signal is analysed according to the ratio of the minimum value of the local energy spectrum to the short-term energy spectrum. After the noise is suppressed according to the normal distribution of Gaussian noise, the logarithmic energy entropy product of adaptive sub-bands is calculated. Using the calculated results as a threshold, compare the logarithmic energy spectral ratio of the current speech frame with the threshold, and use Bayesian classification to detect speech endpoints. Experiment shows that the detection accuracy of this method is always higher than 94.4%, and the accuracy variance is between 0.055 and 0.072, effectively achieving the design expectations.
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